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I, Linguistics Posting Number req25319 Department Linguistics Department Website Link https://linguistics.arizona.edu/ Location Tucson Campus Address Tucson, AZ USA Position Highlights The University
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for participant engagement in long-term implanted brain computer interface trials and participate fully in the lab’s activities across campus. This position requires a doctoral-level degree in a relevant field
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, or computer programming is a plus Technical skills (e.g., Python, R, Unix) are also highly desired but not required Prior experience managing teams of people and/or an interest in psychological and
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this position. WORK ENVIRONMENT: The candidate will work primarily in Newark, NJ and/or New Brunswick, NJ with some remote flexibility. Candidate will have access to a standard computer, phone, and internet
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at the intersection of active transportation safety and AI (e.g., near-miss detection, predictive crash risk, multimodal exposure estimation). · Design, implement, and validate AI pipelines (computer vision
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Remote Sensing; Machine Learning Models for Predicting Wildfire Spread; Wildfire Risk Assessment Through Multi-Modal Data Integration; Automated Vegetation and Fuel Load Mapping Using Computer Vision; AI
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should submit the following documents via Academic Jobs Online: https://academicjobsonline.org/ajo/jobs/31223 · Curriculum Vitae including a list of selected publications · Research Statement · Programming
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science, electrical and computer engineering, sociology, public policy, information science, communication, economics, political science, psychology, philosophy, and related technology disciplines. Selected candidates
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if the position has not yet been filled. Position description Professor Shiva Abbaszadeh in the Department of Electrical and Computer Engineering (ECE) at the University of California, Santa Cruz (UCSC) invites
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computational experiments designed by mathematicians on the team • Interface with mathematicians and applied computer scientists to demonstrate the viability of novel neural networks on challenging benchmark